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Human transporter database: comprehensive knowledge and discovery tools in the human transporter genes.

Ye AY, Liu QR, Li CY, Zhao M, Qu H - PLoS ONE (2014)

Bottom Line: Gene mutations of transporters are often related to pharmacogenetics traits.We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome.Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China ; Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing, China.

ABSTRACT
Transporters are essential in homeostatic exchange of endogenous and exogenous substances at the systematic, organic, cellular, and subcellular levels. Gene mutations of transporters are often related to pharmacogenetics traits. Recent developments in high throughput technologies on genomics, transcriptomics and proteomics allow in depth studies of transporter genes in normal cellular processes and diverse disease conditions. The flood of high throughput data have resulted in urgent need for an updated knowledgebase with curated, organized, and annotated human transporters in an easily accessible way. Using a pipeline with the combination of automated keywords query, sequence similarity search and manual curation on transporters, we collected 1,555 human non-redundant transporter genes to develop the Human Transporter Database (HTD) (http://htd.cbi.pku.edu.cn). Based on the extensive annotations, global properties of the transporter genes were illustrated, such as expression patterns and polymorphisms in relationships with their ligands. We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome. Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

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Related in: MedlinePlus

Distribution of nonsynonyous SNP and CNV density on ten categories of transporter genes in HTD.The x-axis shows the ten transporter categories, and y-axis shows the corresponding value: (A) the density of nonsynonymous SNPs, normalized by dividing CDS length, (B) the density of CNVs, normalized by dividing gene total length. Both subfigures are notched boxplot along with scattered real sample points in purple. The thick band inside the box is the median, and the bottom and top of the box are the first quantile (Q1) and the third quantile (Q3). The ends of the whiskers represents data within 1.5 *IQR ( = Q3–Q1) from the lower quantile (Q1) or the upper quantile (Q3). The notch is always symmetric around the median, with deviation from median by 1.58 *IQR/sqrt(n), where n is the sample size. The notch approximately shows the confidence interval of median, so that if the notches of two boxes do not overlap, their medians are usually significantly different. Three horizontal orange lines behind the boxes show the median and notch range of the “Total” box.
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pone-0088883-g002: Distribution of nonsynonyous SNP and CNV density on ten categories of transporter genes in HTD.The x-axis shows the ten transporter categories, and y-axis shows the corresponding value: (A) the density of nonsynonymous SNPs, normalized by dividing CDS length, (B) the density of CNVs, normalized by dividing gene total length. Both subfigures are notched boxplot along with scattered real sample points in purple. The thick band inside the box is the median, and the bottom and top of the box are the first quantile (Q1) and the third quantile (Q3). The ends of the whiskers represents data within 1.5 *IQR ( = Q3–Q1) from the lower quantile (Q1) or the upper quantile (Q3). The notch is always symmetric around the median, with deviation from median by 1.58 *IQR/sqrt(n), where n is the sample size. The notch approximately shows the confidence interval of median, so that if the notches of two boxes do not overlap, their medians are usually significantly different. Three horizontal orange lines behind the boxes show the median and notch range of the “Total” box.

Mentions: The genetic polymorphisms in transporters often have direct or adverse effects on the pharmacokinetics, drug-drug interactions, and personalized drug treatments [50]. The integration of genetics, disease, and drug information related to transporters provides an overview for the therapeutic safety and efficacy of drugs in various diseases. Based on population SNP information from dbSNP and HapMap, 1,279 genes (82.3%) from 1,555 human transporters overlapped 1,201,561 SNPs, in which 35,358 SNPs are exonic and 19,183 are nonsynonymous. When focusing on nonsynonymous SNPs, the HTGs from “Cytochrome c oxidase”, “Defensin”, and “Mitochondrial translocase” contained significantly less nonsynonymous SNPs in comparison with other transporter genes (Figure S6A). To control the potential influence of CDS length, which was shown different between categories (Figure S6B), we calculated the SNP density by dividing gene CDS length. After normalization, the average nonsynonymous SNP density for “Defensin” was marginally significantly higher than others (Wilcoxon rank sum test, p-value = 0.078), and “Channel” has lower SNP density (p-value = 2.5e-5) (Figure 2A). Copy-number variations (CNVs) refer a structure variation resulting gain or loss of copies of one or more sections of chromosome. Based on the integrated CNV data from DGV database, 855 genes (55.0%) from 1,555 human transporters were overlapped with known CNV regions. With the same analysis approach, after controlling gene total length (Figure S6C, D), CNV density was found significantly higher in “Defensin” (p-value = 7.0e-12), and lower in “Cytochrome c oxidase” (p-value = 3.1e-03) and “Mitochondrial translocase” (p-value = 1.5e-03) (Figure 2B). These results might suggest that “Defensin” genes were subjected to weaker negative selection than other transporter genes.


Human transporter database: comprehensive knowledge and discovery tools in the human transporter genes.

Ye AY, Liu QR, Li CY, Zhao M, Qu H - PLoS ONE (2014)

Distribution of nonsynonyous SNP and CNV density on ten categories of transporter genes in HTD.The x-axis shows the ten transporter categories, and y-axis shows the corresponding value: (A) the density of nonsynonymous SNPs, normalized by dividing CDS length, (B) the density of CNVs, normalized by dividing gene total length. Both subfigures are notched boxplot along with scattered real sample points in purple. The thick band inside the box is the median, and the bottom and top of the box are the first quantile (Q1) and the third quantile (Q3). The ends of the whiskers represents data within 1.5 *IQR ( = Q3–Q1) from the lower quantile (Q1) or the upper quantile (Q3). The notch is always symmetric around the median, with deviation from median by 1.58 *IQR/sqrt(n), where n is the sample size. The notch approximately shows the confidence interval of median, so that if the notches of two boxes do not overlap, their medians are usually significantly different. Three horizontal orange lines behind the boxes show the median and notch range of the “Total” box.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928311&req=5

pone-0088883-g002: Distribution of nonsynonyous SNP and CNV density on ten categories of transporter genes in HTD.The x-axis shows the ten transporter categories, and y-axis shows the corresponding value: (A) the density of nonsynonymous SNPs, normalized by dividing CDS length, (B) the density of CNVs, normalized by dividing gene total length. Both subfigures are notched boxplot along with scattered real sample points in purple. The thick band inside the box is the median, and the bottom and top of the box are the first quantile (Q1) and the third quantile (Q3). The ends of the whiskers represents data within 1.5 *IQR ( = Q3–Q1) from the lower quantile (Q1) or the upper quantile (Q3). The notch is always symmetric around the median, with deviation from median by 1.58 *IQR/sqrt(n), where n is the sample size. The notch approximately shows the confidence interval of median, so that if the notches of two boxes do not overlap, their medians are usually significantly different. Three horizontal orange lines behind the boxes show the median and notch range of the “Total” box.
Mentions: The genetic polymorphisms in transporters often have direct or adverse effects on the pharmacokinetics, drug-drug interactions, and personalized drug treatments [50]. The integration of genetics, disease, and drug information related to transporters provides an overview for the therapeutic safety and efficacy of drugs in various diseases. Based on population SNP information from dbSNP and HapMap, 1,279 genes (82.3%) from 1,555 human transporters overlapped 1,201,561 SNPs, in which 35,358 SNPs are exonic and 19,183 are nonsynonymous. When focusing on nonsynonymous SNPs, the HTGs from “Cytochrome c oxidase”, “Defensin”, and “Mitochondrial translocase” contained significantly less nonsynonymous SNPs in comparison with other transporter genes (Figure S6A). To control the potential influence of CDS length, which was shown different between categories (Figure S6B), we calculated the SNP density by dividing gene CDS length. After normalization, the average nonsynonymous SNP density for “Defensin” was marginally significantly higher than others (Wilcoxon rank sum test, p-value = 0.078), and “Channel” has lower SNP density (p-value = 2.5e-5) (Figure 2A). Copy-number variations (CNVs) refer a structure variation resulting gain or loss of copies of one or more sections of chromosome. Based on the integrated CNV data from DGV database, 855 genes (55.0%) from 1,555 human transporters were overlapped with known CNV regions. With the same analysis approach, after controlling gene total length (Figure S6C, D), CNV density was found significantly higher in “Defensin” (p-value = 7.0e-12), and lower in “Cytochrome c oxidase” (p-value = 3.1e-03) and “Mitochondrial translocase” (p-value = 1.5e-03) (Figure 2B). These results might suggest that “Defensin” genes were subjected to weaker negative selection than other transporter genes.

Bottom Line: Gene mutations of transporters are often related to pharmacogenetics traits.We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome.Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, China ; Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing, China.

ABSTRACT
Transporters are essential in homeostatic exchange of endogenous and exogenous substances at the systematic, organic, cellular, and subcellular levels. Gene mutations of transporters are often related to pharmacogenetics traits. Recent developments in high throughput technologies on genomics, transcriptomics and proteomics allow in depth studies of transporter genes in normal cellular processes and diverse disease conditions. The flood of high throughput data have resulted in urgent need for an updated knowledgebase with curated, organized, and annotated human transporters in an easily accessible way. Using a pipeline with the combination of automated keywords query, sequence similarity search and manual curation on transporters, we collected 1,555 human non-redundant transporter genes to develop the Human Transporter Database (HTD) (http://htd.cbi.pku.edu.cn). Based on the extensive annotations, global properties of the transporter genes were illustrated, such as expression patterns and polymorphisms in relationships with their ligands. We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome. Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

Show MeSH
Related in: MedlinePlus